Automated　annotation　of　skin　biopsy　histopathological　images　provides　valuable　information　and　supports　for　diagnosis,　especially　for　the　discrimination　between　malignant　and　benign　lesions.　Currently,　computer-aid　analysis　of　skin　biopsy　images　mostly　relied　on　some　human-designed　features,　which　requires　expensive　human　efforts　and　experiences　in　problem　domains.　In　this　study,　we　propose　an　annotation　framework　for　automated　skin　biopsy　image　analysis　which　makes　use　of　a　deep　model　for　image　feature　representation.　A　convolutional　neural　network　(CNN)　is　designed　for　local　regions　of　skin　biopsy　images　which　learns　potential　high-level　features　automatically　from　input　raw　pixels.　The　annotation　model　is　constructed　in　the　multiple-instance　multiple-label　(MIML)　learning　framework　with　the　features　learned　through　the　network.　We　achieve　significant　improvement　of　the　model　performance　on　a　real　world　clinical　skin　biopsy　image　dataset　and　a　benchmark　dataset.　Moreover,　our　study　indicates　that　deep　learning　based　model　could　achieve　better　performance　than　human　designed　features.